similar to: Getting all R packages

Hi folks, Suppose I have a series of cases each with categorical factors A, B. What is the best way to "glue" A and B together into a single factor? For example, given A0 B1 ... A1 B1 ... A0 B2 ... A1 B0 ... A0 B0 ... A1 B2 ... then I'd like to end up with a single factor with levels A0B0, A0B1, A0B2, A1B0, A1B1, A1B2 according to all the combinations which actually occur in

Hi folks, I have a vaiable Y and an associated factor Z at several (13) levels. boxplot(Y~Z) produces a nice array of boxplots, one for each level of Z, and each duly labaelled with its level of Z. I would like to superpose on each boxplot the actual data points which it represents, i.e. do something conceptually (though not in real R) expressed as points(Y~Z) or points(Z,Y) It can

I have two CSV files (exported from Excel), say file1 and file2. The have the same number of rows, and each has several columns, with names on the first line; and some of the columns in file1 are repeated in file2. Using the "foreign" package, I can read these in separately to dataframes say d1 and d2 with > d1<-read.csv("file1") >

Greetings Folks, When R code (as entered or read from a courced file) is executed, is it interpreted from the input form every time having once been read in, or do subsequent invocations use an "intermediate" (pre-interpreted) form? Or, putting it another way, is the execution of R code faster second time time round (and later) because the pre-interpretation has already been done once

Can someone clarify what I cannot make out from the documentation? The function 'clogit' in the 'survival' package is described as performing a "conditional logistic regression". Its return value is stated to be "an object of class clogit which is a wrapper for a coxph object." This suggests that its usefulness is confined to the sort of data which arise in

Hi Folks, I'm seeking advice about how to resume an outer loop following failure of a function within the loop (which issues an error message). Essentially, I'm repeating (simulating) a process which involves random sampling, EM and MCMC. I'm walking on very edge of rather thin ice -- data rather thinly spread over many parameters! Occasionally some component of the loop fails, with

Sorry Folks, I'm sure I could suss out the answer myself but I need it soon ... ! 1. Given a set of 4 variables X,Y,Z,W in a dataframe DF, I make a scatter-plot matrix using splom(DF). 2. I do all regressions of U on V using lm(U~V), where U and V are all 12 different ordered pairs from X,Y,Z,W. 3. Now I would like to superpose the regression lines from (2) onto the corresponding

Hi Folks, What is the best way to avoid a function being read in anew (and masking an exiting function) when a definition of it has already been established in R? Reason: Fernando Tusell and I are working up Schafer's 'CAT' for R (basically done now, just needs some cosmetic tidying up). This uses a function 'slice.index', present in S but not in the versions of R we were

Hi Folks, I note that, while the "chisq" functions dchisq(x, df, ncp=0, log = FALSE) pchisq(q, df, ncp=0, lower.tail = TRUE, log.p = FALSE) qchisq(p, df, ncp=0, lower.tail = TRUE, log.p = FALSE) rchisq(n, df, ncp=0) all have a slot for the non-centrality parameter "ncp", of the functions for the t and F distributions: dt(x, df, log = FALSE)

Hi Folks, Sorry to raise what has probably been discussed before, but I an repeatedly struck by the comparative slowness of S-plus for Windows compared with R for Linux when doing much the same thing. I don't have a direct comparison, because they're not running on the same machine; but machine W has a faster CPU and more RAM than machine L, yet S-plus on W seems to take longer by quite

Hi Folks, No doubt a question with a well-known answer, but I'm unfortunately not managing to find it readily ... ! I have a quantitative variable Y and a 4-level ordered factor A (with very unequal numbers at the different levels, by the way). The command lm(Y ~ A) returns (amongst other stuff) an intercept, and coefficients A.L, A.Q and A.C for the Linear, Quadratic and Cubic effects.

Hi Folks, The spreadsheet-like layout displayed when 'data.entry' is invoked is very useful simply for legible display of data, quite apart from its intended use for the purpose of entering or editing data. If one wants to use it _simply_ as a display device, so that one can look around inside a data-set while working on it, then it is not a good idea to have its _editing_ capabilities

Hi Folks, I'm currently looking at missing data/imputation methods (including multiple imputation). S-Plus has a "missing data library". What similar resources are available within R? Or does one roll one's own? Best wishes to all, Ted. -------------------------------------------------------------------- E-Mail: (Ted Harding) <Ted.Harding at nessie.mcc.ac.uk>

Hi Folks, I'm interested in looking (in a beginner's amateurish way) at prosodic/phonetic analysis of recorded speech. In particular I would want to use R to formulate and evaluate specific models. So I would like to ask R people for their recommendations for a program which would a) Take as input a sound file in one of the common formats (".wav", ".au") b)

Hi folks, I use R in X windows on Linux. Normally, I use 'less' as pager, which is fine for scanning through 'help' (or '?') output in the R window itself; the help session is terminated by typing "q", as usual for 'less', and the R window then reverts to the R command line interface. Often, I would like to have the output from 'help' pop up in

Hi folks, I use R in X windows on Linux. Normally, I use 'less' as pager, which is fine for scanning through 'help' (or '?') output in the R window itself; the help session is terminated by typing "q", as usual for 'less', and the R window then reverts to the R command line interface. Often, I would like to have the output from 'help' pop up in

Hi Folks, This is off-topic R-wise, but it may be close to the heart of many R-users, so I think it may be the best place to ask! Users of 'gv' (the "front end" to ghostscript) will be aware of the little window which gives you the x-y coordinates (in points = 1/72 inch) of the position of the "cross-hair" mouse cursor. These coordinates are those of the corresponding

Hi Folks, My recent response to Laura Quinn's query about matrix subsetting reminded of a question. I wrote: iDir <- ((Winds[,20]<45)|(Winds[,20]>315))&(!is.na(Winds[,20])) Now, I find "!is.na" a bit awkward to type, so I might prefer to type it as "nis.na". While it is possible to define nis.na <- function(x){ !is.na(x) } this involves a slight

Hi Folks, I'm trying to understand the model specification formalities for 'lme', and the documentation is leaving me a bit confused. Specifically, using the example dataset 'Orthodont' in the 'nlme' package, first I use the invocation given in the example shown by "?lme": > fm1 <- lme(distance ~ age, data = Orthodont) # random is ~ age Despite the

Hi Folks, While I was browsing in the R-help archives yesterday, I got curious about the time series of the sizes of the monthly archives in MB. This turned out to have an unexpected feature or two, which I leave to readers to explore for themselves. I'm now wondering at what point in time we might expect to be receiving 1000MB/month (30+MB/day). It's not that far away, it seems, but